Tag: AI governance
Shadow Prompting and Data Exfiltration Risks in LLM Workflows
Explore the hidden dangers of shadow prompting and data exfiltration in LLM workflows. Learn how attackers exploit hidden instructions to steal data and how to secure your AI systems.
Centralized Prompt Libraries: Curating Reusable Patterns and Standards for AI Governance
Discover how centralized prompt libraries streamline AI governance by curating reusable patterns. Learn about key features, implementation strategies, and future trends in enterprise prompt management.
Third-Party Risk in Generative AI: Vendor Assessments and Shared Responsibility
Explore how to manage third-party risk in generative AI through effective vendor assessments and a shared responsibility model. Learn practical strategies for securing your AI supply chain.
Roles for Vibe Coding at Scale: AI Champions, Architects, and Verification Engineers
Explore the critical roles of AI Champions, Architects, and Verification Engineers needed to govern vibe coding at scale. Learn how to balance AI speed with security and structure.
Auditing AI Usage: Logs, Prompts, and Output Tracking Requirements
AI auditing requires detailed logs of prompts, outputs, and context to ensure compliance, reduce legal risk, and maintain trust. Learn what to track, which tools work, and how to start without overwhelming your team.
Shadow AI Remediation: How to Bring Unapproved AI Tools into Compliance
Shadow AI is the unapproved use of generative AI tools by employees. Learn how to detect it, bring it into compliance, and avoid massive fines under GDPR, HIPAA, and the EU AI Act with practical steps and real-world examples.
Architectural Standards for Vibe-Coded Systems: Reference Implementations
Vibe coding accelerates development but introduces serious risks without architectural discipline. Learn the five non-negotiable standards, reference implementations, and governance practices that separate sustainable AI-built systems from costly failures.
